Emotion recognition in human–computer interaction

Fragopanagos, N.; Taylor, John G.

doi:10.1016/j.neunet.2005.03.006

Cited by 361 publications

(203 citation statements)

References 16 publications

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“…Whereas on the other side, currently there are no standard classifications of "all human emotions" due to the complex nature of human minds, and any emotion classifications can only be seen as "labels" annotated afterwards for different purposes. Methods used for text based emotion recognition system [4], [5] are:…”

Section: Related Workmentioning

confidence: 99%

Emotion Detection From Text Documents

Shivhare¹,

Saritha²

2014

IJDKP

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Section: Related Workmentioning

confidence: 99%

Emotion Detection From Text Documents

Shivhare¹,

Saritha²

2014

IJDKP

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“…To our knowledge, there are so far only a few works that consider the direct automatic interpretation of facial expressions in terms of valence categories. Caridakis et al [3] and also Fragopanagos & Taylor [10] used the two-dimensional "evaluation-activation space" and investigated the recognition of valence and activation level with neural networks.…”

Section: Related Workmentioning

confidence: 99%

Facial expressions as feedback cue in human-robot interaction—a comparison between human and automatic recognition performances

Lang

Wachsmuth

Wersing

et al. 2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops

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Facial expressions are one important nonverbal communication cue, as they can provide feedback in conversations between people and also in human-robot interaction. This paper presents an evaluation of three standard pattern recognition techniques (active appearance models, gabor energy filters, and raw images) for facial feedback interpretation in terms of valence (success and failure) and compares the results to the human performance. The used database contains videos of people interacting with a robot by teaching the names of several objects to it. After teaching, the robot should term the objects correctly. The subjects reacted to its answer while showing spontaneous facial expressions, which were classified in this work. One main result is that an automatic classification of facial expressions in terms of valence using simple standard pattern recognition techniques is possible with an accuracy comparable to the average human classification rate, but with a high variance between different subjects, likewise to the human performance.

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“…The development of systems capable to identify the emotion of the person who is interacting with, would conduct to respond properly in a more natural way. Thus, these systems could lead to a number of applications for our everyday life (interactive games and entertainment industry), clinical studies for diagnosis of emotional state in psychiatric patients, addition of sensitivity to customer services or call centers, among others [1,2].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Emotion Recognition Using Deep Networks

et al. 2015

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Abstract-In the last years, several efforts have been devoted to the automatic recognition of human emotions. On the one side, there are several works based on speech processing and on the other side, using facial expressions in still images. More recently, other modalities such as body gestures, biosignals and others have been started to be used. In this work we present a multimodal system that process audiovisual information, exploiting the prosodic features in the speech and the development of the facial expressions in videos. The classification of the video in one of six emotions is carried out by deep networks, a neural network architecture consisting of several layers that capture high-order correlations betwen the features. The obtained results show the suitability of the proposed approach for this task, improving the performance of standard multilayer Perceptrons.

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Emotion recognition in human–computer interaction

Cited by 361 publications

References 16 publications

Emotion Detection From Text Documents

Emotion Detection From Text Documents

Facial expressions as feedback cue in human-robot interaction—a comparison between human and automatic recognition performances

Multimodal Emotion Recognition Using Deep Networks

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Emotion recognition in human–computer interaction

Cited by 361 publications

References 16 publications

Emotion Detection From Text Documents

Emotion Detection From Text Documents

Facial expressions as feedback cue in human-robot interaction&#x2014;a comparison between human and automatic recognition performances

Multimodal Emotion Recognition Using Deep Networks

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Facial expressions as feedback cue in human-robot interaction—a comparison between human and automatic recognition performances